ArticleBioMed research international2013
An approach for identifying cytokines based on a novel ensemble classifier.
Article in BioMed research international, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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Who cites it
41 citing papers in PubMed.
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- Ensemble learning-based feature selection for phosphorylation site detection.Frontiers in genetics · 2022Article
- A GHKNN model based on the physicochemical property extraction method to identify SNARE proteins.Frontiers in genetics · 2022Article
- iTTCA-RF: a random forest predictor for tumor T cell antigens.Journal of translational medicine · 2021Article
- Improved cytokine-receptor interaction prediction by exploiting the negative sample space.BMC bioinformatics · 2020Article
- TNFPred: identifying tumor necrosis factors using hybrid features based on word embeddings.BMC medical genomics · 2020Article
- Prediction of G Protein-Coupled Receptors With CTDC Extraction and MRMD2.0 Dimension-Reduction Methods.Frontiers in bioengineering and biotechnology · 2020Article
- Predicting ATP-Binding Cassette Transporters Using the Random Forest Method.Frontiers in genetics · 2020Article
- iPromoter-2L2.0: Identifying Promoters and Their Types by Combining Smoothing Cutting Window Algorithm and Sequence-Based Features.Molecular therapy. Nucleic acids · 2019Article
- An advanced approach to identify antimicrobial peptides and their function types for penaeus through machine learning strategies.BMC bioinformatics · 2019Article
- Identification of Phage Viral Proteins With Hybrid Sequence Features.Frontiers in microbiology · 2019Article
- Predicting Ion Channels Genes and Their Types With Machine Learning Techniques.Frontiers in genetics · 2019Article
- An Ensemble Strategy to Predict Prognosis in Ovarian Cancer Based on Gene Modules.Frontiers in genetics · 2019Article
- RFAmyloid: A Web Server for Predicting Amyloid Proteins.International journal of molecular sciences · 2018Article
- iSS-PC: Identifying Splicing Sites via Physical-Chemical Properties Using Deep Sparse Auto-Encoder.Scientific reports · 2017Article
- Detecting NScientific reports · 2017Article
- PRmePRed: A protein arginine methylation prediction tool.PloS one · 2017Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Biology is meaningful and important to identify cytokines and investigate their various functions and biochemical mechanisms. However, several issues remain, including the large scale of benchmark datasets, serious imbalance of data, and discovery of new gene families. In this paper, we employ the machine learning approach based on a novel ensemble classifier to predict cytokines. We directly selected amino acids sequences as research objects. First, we pretreated the benchmark data accurately. Next, we analyzed the physicochemical properties and distribution of whole amino acids and then extracted a group of 120-dimensional (120D) valid features to represent sequences. Third, in the view of the serious imbalance in benchmark datasets, we utilized a sampling approach based on the synthetic minority oversampling technique algorithm and K-means clustering undersampling algorithm to rebuild the training set. Finally, we built a library for dynamic selection and circulating combination based on clustering (LibD3C) and employed the new training set to realize cytokine classification. Experiments showed that the geometric mean of sensitivity and specificity obtained through our approach is as high as 93.3%, which proves that our approach is effective for identifying cytokines.
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